1. Field of the Disclosure
The present disclosure relates to managing workload and scheduling in a compute environment such as a cluster or grid and more specifically to a system and method of providing an interface between a workload management and scheduling module for a compute environment and an identity manager.
2. Introduction
The present disclosure relates to a system and method of allocation resources in the context of a grid or cluster of computers. Grid computing may be defined as coordinated resource sharing and problem solving in dynamic, multi-institutional collaborations. Many computing projects require much more computational power and resources than a single computer or single processor may provide. Networked computers with peripheral resources such as printers, scanners, I/O devices, storage disks, scientific devices and instruments, etc. may need to be coordinated and utilized to complete a task or a job.
Grid/cluster resource management generally describes the process of identifying requirements, matching resources to applications, allocating those resources, and scheduling and monitoring compute resources over time in order to run applications and workload as efficiently as possible. Each project will utilize a different set of resources and thus is typically unique. In addition to the challenge of allocating resources for a particular job, administrators also have difficulty obtaining a clear understanding of the resources available, the current status of the compute environment and real-time competing needs of various users. One aspect of this process is the ability to reserve resources for a job. A workload manager will seek to reserve a set of resources to enable the compute environment to process a job at a promised quality of service. One example of workload management software is the various compute environment management software available from Cluster Resources, Inc., such as the Moab™ Workload Manager, Moab™ Cluster Manager, the Moab™ Grid Suite and the Moab™ Cluster Suite.
General background information on clusters and grids may be found in several publications. See, e.g., Grid Resource Management, State of the Art and Future Trends, Jarek Nabrzyski, Jennifer M. Schopf, and Jan Weglarz, Kluwer Academic Publishers, 2004; and Beowulf Cluster Computing with Linux, edited by William Gropp, Ewing Lusk, and Thomas Sterling, Massachusetts Institute of Technology, 2003.
It is generally understood herein that the terms grid and cluster are interchangeable in that there is no specific definition of either. In general, a grid will include a plurality of clusters as will be shown in FIG. 1A. Several general challenges exist when attempting to maximize resources in a grid. First, there are typically multiple layers of grid and cluster schedulers. A grid 100 generally includes a group of clusters or a group of networked computers. The definition of a grid is very flexible and may mean a number of different configurations of computers. The definition may depend on how a compute environment is administered and controlled via local control (clusters) or global control/administration (grids). The introduction here is meant to be general given the variety of configurations that are possible.
A grid scheduler 102 communicates with a plurality of cluster schedulers 104A, 104B and 104C. Each of these cluster schedulers communicates with a respective resource manager 106A, 106B or 106C. Each resource manager communicates with a respective series of compute resources shown as nodes 108A, 108B, 108C in cluster 110, nodes 108D, 108E, 108F in cluster 112 and nodes 108G, 108H, 1081 in cluster 114.
Local schedulers (which may refer to either the cluster schedulers 104 or the resource managers 106) are closer to the specific resources 108 and may not allow grid schedulers 102 direct access to the resources. Examples of compute resources include data storage devices such as hard drives and computer processors. The grid level scheduler 102 typically does not own or control the actual resources. Therefore, jobs are submitted from the high level grid-scheduler 102 to a local set of resources with no more permissions that the user would have. This reduces efficiencies and can render the reservation process more difficult. When jobs are submitted from a grid level scheduler 102, there is access information about the person, group or entity submitting the job. For example, the identity of the person submitting the job may have associated with him or her a group of restrictions but also guarantees of service, such as a guarantee that 64 processors will be available within 1 hour of a job submission.
The heterogeneous nature of the shared resources also causes a reduction in efficiency. Without dedicated access to a resource, the grid level scheduler 102 is challenged with the high degree of variance and unpredictability in the capacity of the resources available for use. Most resources are shared among users and projects and each project varies from the other. The performance goals for projects differ. Grid resources are used to improve performance of an application but the resource owners and users have different performance goals: from optimizing the performance for a single application to getting the best system throughput or minimizing response time. Local policies may also play a role in performance.
Within a given cluster, there is only a concept of resource management in space. An administrator can partition a cluster and identify a set of resources to be dedicated to a particular purpose and another set of resources can be dedicated to another purpose. In this regard, the resources are reserved in advance to process the job. By being constrained in space, the nodes108A, 108B, 108C, if they need maintenance or for administrators to perform work or provisioning on the nodes, have to be taken out of the system, fragmented permanently or partitioned permanently for special purposes or policies. If the administrator wants to dedicate them to particular users, organizations or groups, the prior art method of resource management in space causes too much management overhead requiring a constant adjustment of the configuration of the cluster environment and also losses in efficiency with the fragmentation associated with meeting particular policies.
Reservations of compute resources were introduced above. To manage the jobs submissions, a cluster scheduler will employ reservations to insure that jobs will have the resources necessary for processing. FIG. 1B illustrates a cluster/node diagram for a cluster 110 with nodes 120. Time is along the X axis. An access control list (ACL) 114 to the cluster is static, meaning that the ACL is based on the credentials of the person, group, account, class or quality of service making the request or job submission to the cluster. The ACL 114 determines what jobs get assigned to the cluster 110 via a reservation 112 shown as spanning into two nodes of the cluster. Either the job can be allocated to the cluster or it can't and the decision is determined based on who submits the job at submission time. Further, in environments where there are multiple clusters associated with a grid and workload is transferred around the grid, there is a continual difficulty of managing restrictions and guarantees associated with each entity that can submit jobs. Each cluster will have constant alterations made to users and groups as well as modifications of the respective compute environment. Currently, there is no mechanism to insure that up-to-date identity information for a particular user where workload submitted by that user may be transferred to an on-demand site or to a remote cluster from the submitter's local environment.
One deficiency with the prior approach is that there are situations in which organizations would like to make resources available but only in such a way as to balance or meet certain performance goals. Particularly, groups may want to establish a constant expansion factor and make that available to all users or they may want to make a certain subset of users that are key people in an organization and give them special services when their response time drops below a certain threshold. Given the prior art model, companies are unable to have the flexibility over their cluster resources. Further, given the complexity of the interaction between various compute environments, it becomes difficult to insure that the priority identity information associated with the key people will be enforced if workload from those individuals is transferred to another compute environment for processing.
As mentioned above, a challenge in the cluster and grid computing environment relates to management of non-local user credentials for workload. For example, as on-demand computing centers come on-line that enable a cluster or a grid to send jobs or workload in an overflow capacity to the on-demand center, there are situations where non-local users and groups have specific credentials that define constraints on each person or group's rights and limits to use of the compute resources. This may occur, for example, where workload may flow into a compute environment that has non-local user jobs. Where a cluster or one compute environment may communicate with an on-demand center, or a cluster communicating with another cluster, and so forth, there are difficulties in managing and maintaining the constraints on each user's credentials (whether the user is local or non-local) for accessing the local compute environment.
To improve the management of compute resources, what is needed in the art is a system and method for a workload management and scheduling module to manage access to the compute environment according to local and non-local user credentials as the module interacts with other modules and other outside entities.